Translation and optimisation of SEDMORPH's PawlikMorph IDL code for analysing images of galaxies from SDSS data release 7

Replicates the ability to prepare images, generate object binary masks and calculate the shape asymmetry parameters, CAS, gini, M20 and Sersic parameters.

See the Docs for full information.

python imganalysis.py [-h] [-A] [-As] [-Aall] [-sersic] [-spm] [-sci] [-li] [-lif] [-f FILE] [-fo FOLDER] [-src {SDSS,LSST}] [-s IMGSIZE] [-cc CATALOGUE] [-ns NUMSIG] [-fs {1,3,5,7,9,11,13,15}] [-par {multi,parsl,none}] [-n CORES] [-cas] [-segmap][-starmask][-d]

• -h, shows the help screen
• -f FILE, File which contains list of image filenames, and RA DECS of object in image (RA and DEC can be zeros if galaxy is centred on cut-out)
• -fo FOLDER, Path to folder to save script outputs, and where input segmaps and starmasks are located
• -A, runs the asymmetry calculation
• -As, Runs the shape asymmetry calculation
• -Aall, Runs all the implmented asymmetry calculations
• -cas, Calculate CAS parameters
• -spm, Save calculated binary pixelmaps
• -sci, Save cleaned image
• -li, Use larger image cutouts to estimate sky background
• -src, Source of the image
• -cc, Check if any object in the provided catalogue occludes the analysed object
• -sersic, Calculate Sersic profile
• -fs, Size of kernel for mean filter
• -ns, Radial extent to which mask out stars if a catalogue is provided
• -par, Choose which library to use to parallelise script, {multi, parsl, none}
• -n, Number of cores to use
• -segmap, use precomputed segmentation map of galaxy as fits images named 'segmap_' + filename in the OUTPUT folder.
• -starmask, use precomputed star mask as fits images named 'starmask_' + filename in the OUTPUT folder (mutually exclusive to segmap and catalogue)
• -d, create diagnostic figures for each galaxy

Example

This will read images from images.csv, generate a folder sample/output where pixelmaps of the object, clean images, segmentation maps and calculated parameters are stored:

• python imganalysis.py --file images.csv -fo sample -Aall -cas -sci -spm

An example using parallel processing with 8 cores:

• python imganalysis.py --file images.csv -fo sample -Aall -sersic -sci -spm -par parsl -n 8 -cas

First clone this repo. Then either use pip or conda to install dependencies:

• pip install --user --requirement requirements.txt

Or

• conda env create -f environment.yml

In this case you will need to activate the environment before running the code:

• conda activate pawlikMorph-lsst

• Python 3.7+
• Numpy 1.15.4+
• Numba 0.38.1+
• Astropy 3.0.3+
• Scikit-image 0.14.0+
• Pandas 0.25.3+
• photutils 0.7.1
• scipy 1.3.2+
• parsl 0.9.0+

If diagnostic.py is used

• matplotlib